User interface methods and systems for selecting and presenting content based on user navigation and selection actions associated with the content

ABSTRACT

A user-interface method of selecting and presenting a collection of content items based on user navigation and selection actions associated with the content is provided. The method includes associating a relevance weight on a per user basis with content items to indicate a relative measure of likelihood that the user desires the content item. The method includes receiving a user&#39;s navigation and selections actions for identifying desired content items, and in response, adjusting the associated relevance weight of the selected content item and group of content items containing the selected item. The method includes, in response to subsequent user input, selecting and presenting a subset of content items and content groups to the user ordered by the adjusted associated relevance weights assigned to the content items and content groups.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior U.S. patent application Ser.No. 12/326,485 filed on Dec. 2, 2008, entitled User Interface Methodsand Systems For Selecting and Presenting Content Based On UserNavigation and Selection Actions Associated With The Content, now U.S.Pat. No. 7,899,806, which is a continuation of U.S. patent applicationSer. No. 11/738,101 filed on Apr. 20, 2007, entitled User InterfaceMethods and Systems For Selecting and Presenting Content Based On UserNavigation and Selection Actions Associated With The Content, now U.S.Pat. No. 7,461,061, which claims the benefit under 35 U.S.C. §119(e) ofU.S. Provisional Application No. 60/793,537, entitled A System andMethod for Personalized Navigation and Discovery of Information on Inputand Display Constrained Devices, filed Apr. 20, 2006, and U.S.Provisional Application No. 60/793,540, entitled A System and Method forPersonalized Navigation and Discovery of Information Utilizing UsersRelationship to the Information Hierarchy on Input and DisplayConstrained Devices, filed Apr. 20, 2006, the contents of all of whichare incorporated by reference herein.

This application is related to U.S. patent application Ser. No.11/738,138, entitled User Interface Methods and Systems For Selectingand Presenting Content Based On Relationships Between the User and OtherMembers of An Organization, filed Apr. 20, 2007.

BACKGROUND

1. Field of Invention

This invention generally relates to learning the behavior of a usernavigating and selecting content on input and display constraineddevices. More specifically, the invention relates to using the learnednavigation and selection behavior data to personalize the user'sinteractions with various service providers and content query systems,e.g., to better find results to queries provided by the user and toorder the results for presentation to the user.

2. Description of Related Art

The acid test for the usability of an information finding system oninput constrained and display constrained devices is the effort expendedby the user in the discovery of desired information (the discovery ofinformation could be text based search, browsing a content space, orsome combination of both). The effort expended by the user is the numberof steps involved in interacting with an information finding system todiscover the desired information. Each click of a button, or a scrollmotion, or the entry of a character, would be perceived by the user asexpended effort. The success of any user interface may be determined bythis metric.

Minimizing the effort expended to find information (be it search orbrowse) is a challenging problem on input and display constraineddevices such as mobile phones and televisions. The method of discoverythe user chooses may vary upon the application context and the userintent—for example, a user may, from past habit, browse through thephonebook to a contact to make a call (especially when the contact listis small), or perform text input when searching for a web site. Browsebased navigation is typically used (and is effective) when the user'sintent is broad. Furthermore it is a viable form of navigation only whenthe content space is not very large at any level of navigation of thecontent space hierarchy—only text-based search is effective for contentspaces that are large. Any solution however, needs to solve the “minimaleffort” problem for both these forms of discovery.

BRIEF SUMMARY

The invention provides methods and systems for selecting and presentingcontent based on learned user navigation and selection actionsassociated with the content.

Under another aspect of the invention, a user-interface method ofselecting and presenting a collection of content items in which thepresentation s ordered at least in part based on navigation andselection behavior of a user learned over time includes providing a setof content items where each content item has an associated relevanceweight on a per user basis. The method also includes organizing thecontent items into groups based on the informational content of thecontent items, each group of content items having an associatedrelevance weight on a per user basis. The method further includesreceiving from the user navigation and selection actions, adjusting theassociated relevance weight of the selected content item. The methodalso includes, in response to subsequent input entered by the user,selecting and presenting a subset of content items and content groups tothe user where the content items and content groups are ordered at leastin part by the adjusted associated relevance weights assigned to thecontent items and content groups such that content items with greaterassociated relevance weights are presented as more relevant contentitems within a content group and groups of content items with greaterassociated relevance weights are presented as more relevant groups ofcontent items.

Under further aspect of the invention, the context such as geographiclocation of the user, day, date, and time, in which the user performedthe selection action is associated with the adjusted relevance weightingof content items and groups of content items. The adjusted relevanceweighting of content items and groups of content items is only used insubsequent searches by the user when the search is performed in the sameor similar context.

Under yet another aspect of the invention, the adjusted associatedrelevance weights are decayed as time passes from the act of adjustingthe associated relevance weights.

Under yet another aspect of the invention, the adjusted associatedrelevance weights are decayed based upon the number of user selectionsoccurring after the act of adjusting the associated relevance weights.

These and other features will become readily apparent from the followingdetailed description where embodiments of the invention are shown anddescribed by way of illustration.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

For a more complete understanding of various embodiments of the presentinvention, reference is now made to the following descriptions taken inconnection with the accompanying drawings in which:

FIG. 1 illustrates a network based information navigation system inaccordance with one or more embodiments of the invention.

FIG. 2 illustrates device configuration options to perform navigation oflocally or remotely resident information.

FIG. 3A illustrates instances of mobile device interface used to performnavigation of locally or remotely resident information.

FIG. 3B illustrates the various states the user can transition throughto arrive at the desired result through navigation of residentinformation.

FIG. 3C illustrates a 12-key keypad with overloaded keys.

FIG. 4 illustrates a content hierarchy that automatically adjusts itsstructure (from the user's perspective) over time to match the user'spreferences.

FIG. 5 illustrates the user's discovery of information using text searchbefore and after system learns the user's action behavior.

FIG. 6 illustrates the user's discovery of information using browsebefore and after system learns the user's action behavior

FIG. 7 illustrates the user performing a repetitive banking task beforeand after system learns the user's action behavior.

FIG. 8 summarizes the basic concept of personalized navigation.

FIG. 9 illustrates a corporate hierarchy that is being navigated by theuser.

FIG. 10 illustrates the initial conditions and the personalizednavigation as the system continues to learn the user's action behavior.

DETAILED DESCRIPTION

The invention addresses the shortcomings of existing informationnavigation systems by taking a unified approach to the informationfinding process, be it search (incremental or full word search) orbrowse, and helps the user find information of interest by personalizingthe information space to match the user's actions and exploiting therelationship of the user to the information space being navigated. Amulti-pronged holistic approach of taking into account (1) what the userdoes with the device (user's intent) (2) when do these interactionshappen (3) and where do these interactions happen, provides significantinsights into achieving the goal of reducing device interactions, andthereby improving the user experience.

For text-input based discovery content, the key factors to reduce theeffort involved in discovering information is to reduce the number ofcharacters the user has to type in to discover the desired informationand the number of browse navigations to reach the desired result once itappears on the screen. Incremental text search, combined with the rightrelevance ordering of results, is key to reducing the effort involved indiscovering content through text-input based search. For browse baseddiscovery of content, minimizing the number of navigations (navigatingthrough folders and linear scroll) through the browse hierarchy is key.

Preferred embodiments of the invention capture user preferences, userinformation navigation behavior, and a user's relationship to aninformation hierarchy. The learned data is used to personalize theuser's interaction with various service providers and the user'sinteraction with content query systems, e.g., to personalize thenavigation and discovery of information by the user. In an illustrativeembodiment, the user is searching a phonebook for an individual phonenumber. In another illustrative embodiment, the user is an employeesearching a corporate hierarchy for superiors, peers, and subordinates.

Embodiments of the present invention build on techniques, systems andmethods disclosed in earlier filed applications, including but notlimited to U.S. patent application Ser. No. 11/136,261, entitled Methodand System For Performing Searches For Television Programming UsingReduced Text Input, filed on May 24, 2005; U.S. patent application Ser.No. 11/246,432, entitled Method And System For Incremental Search WithReduced Text Entry Where The Relevance Of Results Is A DynamicallyComputed Function of User Input Search String Character Count, filed onOct. 7, 2005; U.S. patent application Ser. No. 11/235,928, entitledMethod and System For Processing Ambiguous, Multiterm Search Queries,filed on Sep. 27, 2005; U.S. patent application Ser. No. 11/509,909,entitled User Interface For Visual Cooperation Between Text Input AndDisplay Device, filed Aug. 25, 2006; and U.S. patent application Ser.No. 11/682,693, entitled Methods and Systems For Selecting andPresenting Content Based On Learned Periodicity Of User ContentSelection, filed on Mar. 6, 2007; the contents of each of which areherein incorporated by reference. Those applications taught specificways to perform incremental searches using ambiguous text input, methodsof ordering the search results, and techniques for learning a user'sbehavior and preferences. The techniques disclosed in those applicationscan be used with the user's navigation behavior or the user'srelationship to an information hierarchy described herein in the same orsimilar ways in which the techniques are applied to the collections ofcontent items described in those applications. In such a case, theuser's behavior or relationship described herein represents a particulartype of content item. The present techniques, however, are not limitedto systems and methods disclosed in the incorporated patentapplications. Thus, while reference to such systems and applications maybe helpful, it is not believed necessary to understand the presentembodiments or inventions.

Referring to FIG. 1, an overall system for navigation of local and/ornetwork resident information using a wide range of devices in accordancewith one or more embodiments of the invention is illustrated. A serverfarm 101 serves as the source of navigation data and relevance updateswith a network 102 functioning as the distribution framework. Thedistribution could be a combination of wired and wireless connections.The navigation devices could have a wide range of interface capabilitiessuch as a hand-held device 103 (e.g. phone, PDA, or a music/videoplayback device) with limited display size and optionally an overloadedor small QWERTY keypad, a television 104 a coupled with a remote 104 bhaving an overloaded or small QWERTY keypad, or a desktop telephone 105with limited display and input entry capabilities.

Referring to FIG. 2, multiple device configurations for search areillustrated. In one configuration, a navigation device 103 has a display201, a processor 202, a volatile memory 203, a text input interface 204which is on-device, remote connectivity 205 to a server 101 through anetwork 102, and a local persistent storage 206. In another deviceconfiguration the device 103 may not have the local persistent storage206. In such a scenario, the device 103 would have remote connectivity205 to submit the query to the server 101 and retrieve results from it.In another configuration of the device 103, it may not have remoteconnectivity 205. In this scenario the navigation database may belocally resident on the local persistent storage 206. The persistentstorage 206 may be a removable storage element, such as SD, SmartMedia,CompactFlash card etc. In a configuration of the device with remoteconnectivity 205 and the local persistent storage 206 for navigation,the device may use the remote connectivity for search/browse relevancedata update or for the case where the navigation database is distributedon the local storage 206 and on the server 101.

FIG. 3A illustrates a mobile device 300A interface for navigatinglocally or remotely resident information. The user enters text using akeypad 302A and the entered text is displayed in a text field 303A. Thenavigation interface on the device is a navigation button 305A thatfacilitates movement minimally in horizontal and vertical directions.The results are displayed in a results area 301A corresponding to theinput incremental text query or browse action. The user can scrollthrough the results using a scroll interface 304A using the navigationbuttons 305A. An alternate scroll interface 307A is shown in a browseonly device 306A (e.g. a music player like iPod where content isremotely resident and the user remotely navigates this content). Thebrowse results are shown in the display 308A.

FIG. 3B illustrates the various states of navigation actions a usercould transition in order to get to the desired result. The user has thefreedom to choose, either exclusively or a combination of, text entryand browse forms of discovery of the result(s) of interest. The actualpath taken however may be influenced by both the user's intent and theinformation that is displayed. For instance, the user may start byentering text 301B, and may scroll through the displayed results 302B,pick a non-terminal node 303B and traverse the children of thenon-terminal 302B. When the user discovers a result, he selects it 303Band performs an appropriate action 304B. This is discussed in furtherdetail in FIG. 5 and FIG. 6.

FIG. 3C illustrates a 12-key keypad with overloaded keys. As explainedin greater detail in the incorporated earlier filed applications akeypad with overloaded keys may be used to perform text input forincremental searches.

Personalized Navigation Based on the User's Navigation Behavior

FIG. 4 graphically illustrates one embodiment of personalized navigationwith the data hierarchy changing over time as a node is repetitivelyacted upon. As described in more detail below, and based on techniquesdescribed in one or more of the incorporated applications, the relevanceweight for a particular node is influenced by the context of eachrepetitive action taken upon the node, including the time and locationof the action. Therefore, the first discoverable node in the phonebooklist can be based on both a user's past navigation behavior along withthe current search location and time. For example U.S. patentapplication Ser. No. 11/682,693, entitled Methods and Systems ForSelecting and Presenting Content Based On Learned Periodicity Of UserContent Selection, filed on Mar. 6, 2007, describes techniques used toinfer future behavior of the user from past behavior.

For illustrative purposes FIG. 4 shows a data hierarchy 400 representedas a tree—it could have been any other form of organization such as agraph. The initial condition of a search space, such as a phonebook, isshown as data nodes (D₁-D_(K)). Each data node is in turn a hierarchy ofnodes of different depths. If a user were to navigate the tree purely asin a browse based fashion, then the number of steps (the cost ofnavigation) to reach a node at depth “i” in a pure browse based fashionis Σ(L_(di)+N_(i)) (1<=i<=I) where L is the cost of linear traversal toa node at level “i” where the traversed node is an ancestor of the nodeof interest, and N is the cost of descending from an ancestor of thenode to the first child in its immediate descendants list. This cost isprimarily due to the linearization of the data hierarchy as the userdescends down the tree by a pure browse action.

For example, a data hierarchy 401 is representative of a phonebook withdata node D_(k) representing John Doe, child node C_(k)J representingJohn Doe's mobile phone number, and the other child node siblings tochild node C_(k)J representing John Doe's other contact information suchas home and office numbers. The data hierarchy also contains node D₁representing John Adams and node D₂ representing John Brown. The userinterface could, from a rendering perspective, display both a contact(e.g. data nodes D₁ through D_(k)) and the associated primary contactnumber (e.g. child nodes C₁l through C_(k)l). For example, when the usersearches for “John” the result set 402 would contain D₁ (John Adams) andC_(i)l (John Adams' primary contact number), D₂ (John Brown) and C₂ 1(John Brown's primary contact number), up to and including D_(k) (JohnDoe) and C_(k)l (John Doe's primary contact number). The user would havethe option to either see other contact numbers for John Doe bydescending down the tree, or directly making a call to the primarycontact number initially presented.

If the user is interested in John Doe's mobile phone number, nodeC_(k)J, the user may discover the number using a text search or browsebased navigation. In addition, if the user repetitively searches for orbrowses to node C_(k)J, the relevance weight assigned to this node wouldcontinue to strengthen with each repetitive action taken upon it. Theincreased relevance weight assigned to the node would be used to reorderthe view of the navigation hierarchy from the user's perspective. Asillustrated 401 prior to the learned preference and increased relevanceweight, the node C_(k)J would be the j^(th) entry presented in JohnDoe's list of contact numbers. As illustrated in 403, after theincreased relevance weight is applied, node C_(k)J would bubble up to bethe first entry within node D_(k), e.g. becoming the first phone numberin John Doe's contact folder. The result set 404 displayed for data nodeD_(k) (John Doe) would now present John Doe's mobile phone number as thefirst entry in the result set.

As illustrated in 405, node C_(k)J's weight would continue to strengthenwith usage and eventually this node would become the first discoverablenode in the phonebook list. After learning has taken place the resultset 406 would have D_(k) (John Doe) and C_(k)J (John Doe's mobilenumber) as the first entry. The remainder of the result set, absent anyother user selections, would contain D₁ (John Adams) and C₁l (JohnAdams' primary contact number), and D₂ (John Brown) and C₂ 1 (JohnBrown's primary contact number).

Repetitive actions with regular patterns eventually result in the usernot even having to do much. The relevant nodes receive an increasedweight and the contact number would be rendered on the phone display atthe appropriate time and location. It is important to note that thisstrengthening of the relevance weight of the node happens regardless ofthe type of navigation, either search or browse. Both result in the sameform of reorganized view of the navigation hierarchy. For example, ifthe user always searches for John Doe and calls him, the increase inrelevance weight of John Doe would result in John Doe being discoveredwith fewer characters. Finally, if the repetitive pattern is veryregular, the text input step may even be eliminated. The first node inthe phonebook context would contain John Doe's contact information andthe user would just have to select the contact without entering anyincremental text.

While the above illustration focuses on reordering for highly repetitivetasks, the system could also perform reordering of the user's view ofthe content space based on the broader knowledge of the user's tasteslearned from the user's action patterns. For example, if the user alwayssearches for action genre movies, then those movies could be given morerelevance so as to be discovered more easily.

FIG. 5 illustrates a user performing text input based incremental searchon a mobile phone. The user types in ‘R’ (501), which results in therendering of results starting with R. FIGS. 502-504 illustrate the userscrolling down to the last row to select RANDOLF to make a call. If this“search-call” action were a repetitive action (with time and locationalso considered), the system would learn this over time 505 and boostthe relevance of RANDOLF so that the user can find this contact easily.Furthermore, if there is another person “RAMA” who is also called in aregular manner but in a different time window, then the entry of thesame character “R” would bring up RANDOLF at 9:00 am (which is typicallywhen a call is made to Randolf) and “Rama” at 5:00 pm (which istypically when a call is made to this person). Over time, thisrepetitive action would strengthen enough to even obviate the user tohave to enter the character “R”. Around 9:00 am in the morning, thephone would display “Randolf” on the top for easy access, and “Rama”around 5:00 pm. Thus, in this example, the presentment of the datahierarchy at 9:00 am is different from the presentment of the datahierarchy at 5:00 pm.

The time window identified for repetitive actions may be defined inadvance or may be determined dynamically according to the frequency ofthe repetitive actions. For example, the time window may be set as15-minute periods occurring during each day or the system may determinea larger window is appropriate for a particular day. The time window mayalso be differentiated by day of the week or date, e.g., different nodesmay be of higher relevance during the week as compared to theirrelevance during the weekend. Finally, the system may interface withexternal applications and determine an ideal time window based on thedata in the application. For example, the system may take data from acalendar application and boost the relevance of nodes based upon aweekly, monthly, or annual event (such as a birthday of a family memberor a monthly project meeting).

Similarly, location of the user may influence the relevance of a node.For example, if the user is at work, the relevance of business contactinformation may be increased. Location may be determined by a variety ofmethods well known in the art, e.g., the user's device may have GPScapabilities.

FIG. 6 illustrates the learning of a browse based repetitive navigationbehavior. Steps 601 through 604 illustrate a user initially performingscroll (or page down operations) through a lexicographically sorted listof contacts to reach the desired contact, Randolf. The system learnsthis action behavior 605 over time, and automatically displays“Randolf's” number on the screen for easier access. Similarly, if thecontact “Rama” were typically called at a different time window, then“Rama” would be displayed for easy access during that period of day.Thus from a lexicographic ordering (initial condition) the system learnsthe user's action patterns over time, to render orderings that reflectthe most likely actions the user would perform for that particular timeand location.

FIG. 7 illustrates another instance where a search and browse basednavigation to reach an action is learned by the system and optimized toprovide a better user experience. The user performs an incrementalsearch 701, entering “BANKE” which results in discovering the bank ofchoice “BANKEX.” The user then browses to the bank finance portion ofthe bank “B. FINANCE,” 702 followed by selecting the payment item “PAY”from the submenu 703. The first step of making a payment is pin entry704. As the system learns 705 the user's actions over time, the user'seffort expended is reduced both in the number of characters used to getto the desired bank (“BA”) and the number of browse actions the userneeds to perform—the bill pay option is shown along with the bank in theresult. The user now has to enter three fewer characters during theincremental text search, and does not need to browse through twosubmenus to select the payment item. This form of rendering an aggregatenode, along with the most likely child node that user may act upon,gives the user the choice to act on both items without further effort.The user can select the pay bill option or choose to browse all theother choices.

FIG. 8 summarizes the basic concept of personalizing the content spaceof the user based on learned behavior. The user's navigation through thecontent space 801 and actions on discovered content 802 is learned bythe system. Each time a user performs an action on a node, the relevanceof that node (for that particular time and location), with respect tothe user is altered. The view of the content hierarchy from the user'sperspective is altered to match the user's learned behavior andpreferences.

Personalized Navigation Based on the User's Relationship to theInformation Hierarchy

FIG. 9 illustrates an instance of a corporate employee hierarchy treewith a user 904 at a particular level in this hierarchy, specificallylevel 2. Tom Dalton 901 is at level 0, Tom Clancy 902 is at level 1, TomJones 903 is a peer of the user 904 at level 2, and Tom Crawford 905 isa direct report of the user 904 on level 3. In the initial conditionsstate, a user who just joins a company or moves to a new position withina company, can benefit from the present invention, which adjusts theorganization tree to help the user 904 easily find the members of thegroup at his level, his direct reports, or his manager. The user caneasily find the person he is looking for by entering just a fewcharacters of an incremental search or with minimal steps using a purebrowse search.

When the user 904 searches for a particular person by entering text,e.g., “TOM”, the system automatically lists the results in descendingorder of the proximity of the matched employee(s) in relationship to theuser's position in the hierarchy. However, after learning, the nodesthat are immediate descendents to the user's node may trump the user'ssibling nodes, since the immediate descendents may be direct reports.Additionally, if the user 904 is discovering the information using anincremental search, e.g., “TO”, results may be shown with matches fromdifferent nodes as clusters for each level with one match displayed withthe aggregate node (e.g. TOM CLANCY at Level 1, TOM CRAWFORD Level 3,TOM DALTON level 0). The system may provide a means to navigate theseaggregate nodes, so the user can quickly get to any level. If the useris navigating the tree purely by a browse means, then the employees atthe user's level (or his immediate reports) will be listed first asaggregates followed by other levels. This form of navigation would bemore user-friendly than a pure lexicographically ordered browse tree.

The user search experience is also improved, in comparison to pureorganization based clustering, by reordering the information hierarchyto match the user's repetitive action behavior. For example, if the user904 repetitively navigates to a sibling node to perform an action (e.g.navigating to the node for Tom Jones 903 to place a phone call), thenthe ordering of the user's siblings would be adjusted over time toreduce this navigation distance by bringing that node closer to theuser. This approach can also be used for any node that is at any level.For example, if the user 904 always navigates to the node for Tom Clancy902 to place a phone call, then that node is reordered at its own levelto come up quicker. Additionally with time, the nodes that arefrequently visited in the hierarchy would move closer to the user's homenode 904.

The navigation process within the corporate employee hierarchy treecould have been text-based search or browse based navigation. Over timethe nodes that are frequently visited in the hierarchy would move closerto the user's home node within the hierarchy, thus easing theirdiscovery either by search or browse. If the search were an incrementalsearch, over time personalization would reduce the number of charactersrequired for discovering the node. If the search was a browsed basednavigation, over time personalization would reduce the number of userselections required for discovering the node.

FIG. 10 illustrates the evolution of the navigation system with time1002 as the navigation hierarchy is reordered to match the user's actionbehavior (e.g. making a phone call after discovering a node ofinterest). In an embodiment of the invention, the initial conditions1001 would start with the “locus of relevance centered” at the user'sposition in the organization hierarchy. As the user navigates hierarchand selects specific content items, the hierarchy would evolve to bringreferenced nodes closer to user's locus of relevance. For example, usingthe method described in FIG. 4 above, this could be accomplished byassigning initial relevance weights to nodes in the navigation hierarchybased on the users position within a corporate hierarchy. The initialrelevance weights could be assigned such that, prior to learning, theresults would be ordered to return peers, then subordinates, thensupervisors, and finally persons unrelated to the user in the corporatehierarchy. As the user navigates and selects content items from thenavigation hierarchy the relevance weights of particular nodes, withrespect to the particular user performing the search, would beincreased. When the user makes subsequent searches, the nodes withhigher relevance weights would be presented higher in the searchresults.

In another embodiment of the invention the locus of relevance wouldalways remain at the root of the organization hierarchy, with the user'snodes of interest hoisted to the root for easy access. This method ofreordering would be meaningful for information finding in anentertainment space, where no prior knowledge of the user's interest isknown, and hence there is no a priori relationship between the user andthe content navigation hierarchy.

Another instance of automatic adjustment of circle of relevance is wherethe user is part of a defined group, for example, where the user is amember of an Instant Messaging group or an online community group, suchas a Yahoo group. The system would automatically increase the relevanceweights of the members of the group in relation to the user. Here theadjustment of the circle of relevance is done by the system merely bythe participation of the user in these groups and no explicit action bythe user is required. This is similar to the corporate setting where auser can be grouped with his or her peers, or where a user can begrouped with all other employees with offices on the same floor in abuilding.

Additionally, the system can take advantage of dynamic groups createdfor projects spanning employees in the corporate hierarchy. The membersof these dynamically created groups would also move closer to the “locusof relevance” of the user. These groups could have been createdexplicitly in the corporate database, or the system may interface withexternal applications, such as a mailing list in an email application,in order to discover these dynamic groups. Once a dynamically createdgroup is detected, again using the techniques described above, therelevance weights of the members of that group can be adjusted such thatgroup members are returned higher in the result set, overriding thedefault corporate hierarchy. For example, after a new emailing list fora project is created, the relevance weights of the members of thatproject can be adjusted and the results would be ordered to returnproject members, then peers, then subordinates, then supervisors, andfinally persons unrelated to the user in the corporate hierarchy.

Automatic adjustment of locus or circle of relevance would also beapplied in a transitive manner between individuals or groups ofindividuals based on the actions of the individuals. For example, in acommunity, if a Susie calls Barbara often, and Barbara calls Kate often,then the likelihood of Susie calling Kate increases over time. Hence,when Susie makes a search or performs a browse, the relevance ofordering of Kate is increased, such that Susie can discover Kate moreeasily. In this case, when Susie navigates and selects the contactinformation for Barbara, the relevance weight for that node is adjusted.In addition, the relevance weights for any nodes that Barbara hasselected, e.g. Kate, are also increased with respect to Susie. Thecontact information for both Barbara and Kate will now be returnedhigher in the result set for any subsequent searches by Susie.

In an embodiment of the invention the locus of relevance would also beadjusted over time by the system taking into account the actions takenby groups of individuals. For example, if members of two groups in anorganization hierarchy communicate often with each other (e.g. theaction taken by users in this case being making a phone call), then thetwo groups would come closer to each other in the navigation hierarchy.So when searches are done by a member of one of these groups, the systemwould give a higher relevance to people from the other group with whichthe communication was high—this would facilitate the discovery of thedesired result with fewer characters in the case of incremental search.Similarly, in a browse based discovery, the other group would be foundcloser to the user's own group in the organization hierarchy.

For example, consider a corporate hierarchy where Able and Baker aremembers of the accounting department, Charlie and Dawn are members ofthe tax department, and Eugene is a member of the legal department. IfAble calls Charlie on a regular basis then the accounting and taxdepartments become closer to each other in the navigation hierarchy.Here the relevance weights for all members of both departments areadjusted, not just those for Able and Charlie. So when Baker searchesthe corporate hierarchy members of the tax department will have a higherrelevance than members of the legal department. This is due to thecontacts, over time, between members of the two departments, e.g. thecontacts between Able and Charlie, and the associated adjustments to therelevance weights for all members of both departments.

Having described preferred embodiments of the present invention, itshould be apparent that modifications can be made without departing fromthe spirit and scope of the invention. For example, the relativeweighting of nodes has been used herein in the context of a phone book.However, embodiments of the invention can be implemented for any form ofnode based content space, such as genres of movies.

1. A user-interface method of selecting and presenting a collection ofitems in which the presentation is ordered at least in part based onnavigation and selection behavior of a user learned over time, themethod comprising: providing access to a set of items; associating aninitial relevance weight, on a per user basis, with each of a pluralityof the items of the set to indicate a relative measure of a likelihoodthat the item is desired by the user; receiving input entered by theuser for identifying desired items; in response to the input entered bythe user, selecting and presenting a subset of items to the user as anavigable topology of items wherein the items are ordered at least inpart by the initial associated relevance weights associated with theitems; receiving input entered by the user for navigating through thetopology of the subset of items and for identifying and selecting thedesired items; in response to a selection by the user of an item,presenting said item to the user and adjusting the associated relevanceweight of said item; subsequent to adjusting the associated relevanceweight of any of the items, selecting and presenting a subset of itemsto the user wherein the items are ordered at least in part by theadjusted associated relevance weights assigned to the items such thatitems with greater associated relevance weights are presented as morerelevant items.
 2. A user-interface system for selecting and presentinga collection of items in which the presentation is ordered at least inpart based on navigation and selection behavior of a user learned overtime, the system comprising: logic for providing access to a set ofitems; logic for associating an initial relevance weight, on a per userbasis, with each of a plurality of the items of the set to indicate arelative measure of a likelihood that the item is desired by the user;logic for receiving input entered by the user for identifying desireditems; logic, responsive to the input entered by the user, for selectingand presenting a subset of items to the user as a navigable topology ofitems wherein the items are ordered at least in part by the initialassociated relevance weights associated with the items; logic forreceiving input entered by the user for navigating through the topologyof the subset of items and for identifying and selecting the desireditems; logic, responsive to a selection by the user of an item, forpresenting said item to the user and for adjusting the associatedrelevance weight of said item; logic for selecting and presenting asubset of items to the user subsequent to adjusting the associatedrelevance weight of any of the items, wherein the items are ordered atleast in part by the adjusted associated relevance weights assigned tothe items such that items with greater associated relevance weights arepresented as more relevant items.
 3. The method of claim 1, furthercomprising: organizing the set of content items into content groupsbased on the information content of the content items; associating aninitial relevance weight, on a per user basis, with at least one of thecontent groups to indicate a relative measure of a likelihood that atleast one content item of the content group is desired by the user; inresponse to the input entered by the user, selecting and presenting asubset of content groups within the navigable topology wherein thecontent groups are ordered at least in part by the initial associatedrelevance weights associated with the content groups; in response to theselection by the user of an item, adjusting the relevance weightassociated with the content group containing the selected content item;and subsequent to adjusting the associated relevance weight of any ofthe content groups, selecting and presenting a subset of content groupsto the user wherein the groups are ordered at least in part by theadjusted associated relevance weights assigned to the content groupssuch that groups with greater associated relevance weights are presentedas more relevant groups.
 4. The method of claim 1, further comprising:determining a context in which the user preformed the selection actions,the context including at least one of geographic location of the user,day, date, and time; and associating the contexts of the user selectionactions with the adjusted relevance weighting of content items learnedfrom the corresponding user selections; wherein only adjusted relevanceweightings associated with the context in which the user enters asubsequent input are used in the selecting and ordering of the contentitems.
 5. The method of claim 1, wherein the adjusted associatedrelevance weights are decayed as time passes from the act of adjustingthe associated relevance weights.
 6. The method of claim 1, wherein theadjusted associated relevance weights are decayed based upon the numberof user selections occurring after the act of adjusting the associatedrelevance weights.
 7. The system of claim 2, further comprising: logicfor organizing the set of content items into content groups based on theinformation content of the content items; logic for associating aninitial relevance weight, on a per user basis, with at least one of thecontent groups to indicate a relative measure of a likelihood that atleast one content item of the content group is desired by the user;logic, responsive to the input entered by the user, for selecting andpresenting a subset of content groups within the navigable topology,wherein the content groups are ordered at least in part by the initialassociated relevance weights associated with the content groups; logic,responsive to the selection by the user of an item, for adjusting therelevance weight associated with the content group containing theselected content item; and logic for selecting and presenting a subsetof content groups to the user subsequent to adjusting the associatedrelevance weight of any of the content groups, wherein the groups areordered at least in part by the adjusted associated relevance weightsassigned to the content groups such that groups with greater associatedrelevance weights are presented as more relevant groups.
 8. The systemof claim 2, further comprising: logic for determining a context in whichthe user preformed the selection actions, the context including at leastone of geographic location of the user, day, date, and time; and logicfor associating the contexts of the user selection actions with theadjusted relevance weighting of content items learned from thecorresponding user selections; wherein only adjusted relevanceweightings associated with the context in which the user enters asubsequent input are used in the selecting and ordering of the contentitems.
 9. The system of claim 2, further comprising logic for decayingthe adjusted associated relevance weights as time passes from the act ofadjusting the associated relevance weights.
 10. The system of claim 2,further comprising logic for decaying the adjusted associated relevanceweights based upon the number of user selections occurring after the actof adjusting the associated relevance weights.